Overview

Dataset statistics

Number of variables21
Number of observations118224
Missing cells1029461
Missing cells (%)41.5%
Duplicate rows47
Duplicate rows (%)< 0.1%
Total size in memory19.8 MiB
Average record size in memory176.0 B

Variable types

Numeric19
Categorical2

Alerts

ControlBoxTemperature has constant value "0.0"Constant
WTG has constant value "G01"Constant
Dataset has 47 (< 0.1%) duplicate rowsDuplicates
ActivePower is highly overall correlated with BearingShaftTemperature and 8 other fieldsHigh correlation
AmbientTemperatue is highly overall correlated with HubTemperature and 1 other fieldsHigh correlation
BearingShaftTemperature is highly overall correlated with ActivePower and 9 other fieldsHigh correlation
Blade1PitchAngle is highly overall correlated with Blade2PitchAngle and 1 other fieldsHigh correlation
Blade2PitchAngle is highly overall correlated with Blade1PitchAngle and 1 other fieldsHigh correlation
Blade3PitchAngle is highly overall correlated with Blade1PitchAngle and 1 other fieldsHigh correlation
GearboxBearingTemperature is highly overall correlated with ActivePower and 8 other fieldsHigh correlation
GearboxOilTemperature is highly overall correlated with ActivePower and 9 other fieldsHigh correlation
GeneratorRPM is highly overall correlated with ActivePower and 8 other fieldsHigh correlation
GeneratorWinding1Temperature is highly overall correlated with ActivePower and 9 other fieldsHigh correlation
GeneratorWinding2Temperature is highly overall correlated with ActivePower and 9 other fieldsHigh correlation
HubTemperature is highly overall correlated with AmbientTemperatue and 5 other fieldsHigh correlation
MainBoxTemperature is highly overall correlated with AmbientTemperatue and 1 other fieldsHigh correlation
NacellePosition is highly overall correlated with WindDirectionHigh correlation
ReactivePower is highly overall correlated with ActivePower and 8 other fieldsHigh correlation
RotorRPM is highly overall correlated with ActivePower and 8 other fieldsHigh correlation
WindDirection is highly overall correlated with NacellePositionHigh correlation
WindSpeed is highly overall correlated with ActivePower and 8 other fieldsHigh correlation
ActivePower has 23474 (19.9%) missing valuesMissing
AmbientTemperatue has 24407 (20.6%) missing valuesMissing
BearingShaftTemperature has 55706 (47.1%) missing valuesMissing
Blade1PitchAngle has 76228 (64.5%) missing valuesMissing
Blade2PitchAngle has 76333 (64.6%) missing valuesMissing
Blade3PitchAngle has 76333 (64.6%) missing valuesMissing
ControlBoxTemperature has 56064 (47.4%) missing valuesMissing
GearboxBearingTemperature has 55684 (47.1%) missing valuesMissing
GearboxOilTemperature has 55786 (47.2%) missing valuesMissing
GeneratorRPM has 55929 (47.3%) missing valuesMissing
GeneratorWinding1Temperature has 55797 (47.2%) missing valuesMissing
GeneratorWinding2Temperature has 55775 (47.2%) missing valuesMissing
HubTemperature has 55818 (47.2%) missing valuesMissing
MainBoxTemperature has 55717 (47.1%) missing valuesMissing
NacellePosition has 45946 (38.9%) missing valuesMissing
ReactivePower has 23476 (19.9%) missing valuesMissing
RotorRPM has 56097 (47.4%) missing valuesMissing
TurbineStatus has 55316 (46.8%) missing valuesMissing
WindDirection has 45946 (38.9%) missing valuesMissing
WindSpeed has 23629 (20.0%) missing valuesMissing
TurbineStatus is highly skewed (γ1 = 177.6547686)Skewed
RotorRPM has 2809 (2.4%) zerosZeros

Reproduction

Analysis started2023-05-29 17:00:02.999399
Analysis finished2023-05-29 17:00:27.967023
Duration24.97 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

ActivePower
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct94084
Distinct (%)99.3%
Missing23474
Missing (%)19.9%
Infinite0
Infinite (%)0.0%
Mean619.10981
Minimum-38.524659
Maximum1779.0324
Zeros594
Zeros (%)0.5%
Negative15644
Negative (%)13.2%
Memory size1.8 MiB
2023-05-29T13:00:28.020396image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-38.524659
5-th percentile-6.4186291
Q179.642258
median402.65489
Q31074.5918
95-th percentile1720.2046
Maximum1779.0324
Range1817.5571
Interquartile range (IQR)994.94952

Descriptive statistics

Standard deviation611.27537
Coefficient of variation (CV)0.98734565
Kurtosis-0.94310208
Mean619.10981
Median Absolute Deviation (MAD)398.88313
Skewness0.70976813
Sum58660654
Variance373657.58
MonotonicityNot monotonic
2023-05-29T13:00:28.142278image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 594
 
0.5%
1730.6644 44
 
< 0.1%
-0.0003023227 8
 
< 0.1%
1575.5742 5
 
< 0.1%
1737.2281 3
 
< 0.1%
-4.5095987 3
 
< 0.1%
0.00043881143 3
 
< 0.1%
653.9309 3
 
< 0.1%
1725.29982 2
 
< 0.1%
651.8255 2
 
< 0.1%
Other values (94074) 94083
79.6%
(Missing) 23474
 
19.9%
ValueCountFrequency (%)
-38.52465939 1
< 0.1%
-38.31229862 1
< 0.1%
-29.61263391 1
< 0.1%
-22.1754245 1
< 0.1%
-19.5226848 1
< 0.1%
-15.5358763 1
< 0.1%
-15.099586 1
< 0.1%
-14.61247716 1
< 0.1%
-14.59247 1
< 0.1%
-13.87283633 1
< 0.1%
ValueCountFrequency (%)
1779.032433 1
< 0.1%
1768.1444 1
< 0.1%
1767.2888 1
< 0.1%
1757.2687 1
< 0.1%
1756.9816 1
< 0.1%
1749.25321 1
< 0.1%
1749.1084 1
< 0.1%
1748.4562 1
< 0.1%
1747.7269 1
< 0.1%
1746.1604 1
< 0.1%

AmbientTemperatue
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct93678
Distinct (%)99.9%
Missing24407
Missing (%)20.6%
Infinite0
Infinite (%)0.0%
Mean28.774654
Minimum0
Maximum42.405597
Zeros16
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-05-29T13:00:28.205393image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.280221
Q125.627428
median28.340541
Q331.664772
95-th percentile36.882189
Maximum42.405597
Range42.405597
Interquartile range (IQR)6.0373436

Descriptive statistics

Standard deviation4.3691449
Coefficient of variation (CV)0.15184005
Kurtosis-0.012360167
Mean28.774654
Median Absolute Deviation (MAD)3.0111426
Skewness0.35216295
Sum2699551.7
Variance19.089428
MonotonicityNot monotonic
2023-05-29T13:00:28.263376image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.844826 44
 
< 0.1%
27.792393 24
 
< 0.1%
0 16
 
< 0.1%
32.6452 9
 
< 0.1%
27.792393 8
 
< 0.1%
37.102978 7
 
< 0.1%
22.40376 6
 
< 0.1%
29.266325 5
 
< 0.1%
35.003857 3
 
< 0.1%
36.194653 3
 
< 0.1%
Other values (93668) 93692
79.2%
(Missing) 24407
 
20.6%
ValueCountFrequency (%)
0 16
< 0.1%
3.030066906 × 10-201
 
< 0.1%
11.13068395 1
 
< 0.1%
16.1116675 1
 
< 0.1%
18.1019715 1
 
< 0.1%
18.227419 1
 
< 0.1%
18.61710611 1
 
< 0.1%
18.71038822 1
 
< 0.1%
18.71183878 1
 
< 0.1%
18.7926749 1
 
< 0.1%
ValueCountFrequency (%)
42.4055965 1
< 0.1%
42.0209375 1
< 0.1%
42.00404011 1
< 0.1%
41.9483075 1
< 0.1%
41.92922722 1
< 0.1%
41.8949063 1
< 0.1%
41.8373459 1
< 0.1%
41.8097136 1
< 0.1%
41.78824589 1
< 0.1%
41.7411471 1
< 0.1%

BearingShaftTemperature
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct62286
Distinct (%)99.6%
Missing55706
Missing (%)47.1%
Infinite0
Infinite (%)0.0%
Mean43.010189
Minimum0
Maximum55.088655
Zeros225
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-05-29T13:00:28.321619image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35.800026
Q139.840247
median42.910877
Q347.007976
95-th percentile50.65371
Maximum55.088655
Range55.088655
Interquartile range (IQR)7.1677285

Descriptive statistics

Standard deviation5.5453118
Coefficient of variation (CV)0.12893019
Kurtosis12.287511
Mean43.010189
Median Absolute Deviation (MAD)3.4488811
Skewness-1.9071463
Sum2688911
Variance30.750483
MonotonicityNot monotonic
2023-05-29T13:00:28.376575image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 225
 
0.2%
43.509945 3
 
< 0.1%
50.526719 2
 
< 0.1%
40.17997 2
 
< 0.1%
50.352311 2
 
< 0.1%
48.221169 2
 
< 0.1%
42.09256887 2
 
< 0.1%
44.817422 2
 
< 0.1%
40.57874 1
 
< 0.1%
40.32604863 1
 
< 0.1%
Other values (62276) 62276
52.7%
(Missing) 55706
47.1%
ValueCountFrequency (%)
0 225
0.2%
13.51817567 1
 
< 0.1%
13.688085 1
 
< 0.1%
15.31223667 1
 
< 0.1%
16.119878 1
 
< 0.1%
16.805773 1
 
< 0.1%
17.263845 1
 
< 0.1%
18.43944 1
 
< 0.1%
18.901865 1
 
< 0.1%
19.473595 1
 
< 0.1%
ValueCountFrequency (%)
55.088655 1
< 0.1%
55.065134 1
< 0.1%
55.015868 1
< 0.1%
55.0057808 1
< 0.1%
54.9823555 1
< 0.1%
54.941518 1
< 0.1%
54.9390186 1
< 0.1%
54.9277441 1
< 0.1%
54.91379785 1
< 0.1%
54.8954128 1
< 0.1%

Blade1PitchAngle
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38946
Distinct (%)92.7%
Missing76228
Missing (%)64.5%
Infinite0
Infinite (%)0.0%
Mean9.7496407
Minimum-43.156734
Maximum90.14361
Zeros20
Zeros (%)< 0.1%
Negative18982
Negative (%)16.1%
Memory size1.8 MiB
2023-05-29T13:00:28.433748image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-43.156734
5-th percentile-1.095369
Q1-0.93984897
median0.39439939
Q38.0993023
95-th percentile61.11313
Maximum90.14361
Range133.30034
Interquartile range (IQR)9.0391512

Descriptive statistics

Standard deviation20.644828
Coefficient of variation (CV)2.1174963
Kurtosis5.1858405
Mean9.7496407
Median Absolute Deviation (MAD)1.4278086
Skewness2.4164882
Sum409445.91
Variance426.20893
MonotonicityNot monotonic
2023-05-29T13:00:28.493930image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.71959 245
 
0.2%
88.71959 129
 
0.1%
90.14361 78
 
0.1%
85.00009 71
 
0.1%
88.71959 68
 
0.1%
85.00005 60
 
0.1%
88.71959 52
 
< 0.1%
60.000126 50
 
< 0.1%
88.74794 44
 
< 0.1%
60.000076 43
 
< 0.1%
Other values (38936) 41156
34.8%
(Missing) 76228
64.5%
ValueCountFrequency (%)
-43.15673384 1
< 0.1%
-1.9604657 2
< 0.1%
-1.852215075 1
< 0.1%
-1.576649099 1
< 0.1%
-1.488250842 1
< 0.1%
-1.481081575 1
< 0.1%
-1.475139354 1
< 0.1%
-1.469609312 1
< 0.1%
-1.454062832 1
< 0.1%
-1.446407394 1
< 0.1%
ValueCountFrequency (%)
90.14361 1
 
< 0.1%
90.14361 78
0.1%
90.14361 17
 
< 0.1%
89.11526 1
 
< 0.1%
89.02752 1
 
< 0.1%
89.00871 2
 
< 0.1%
88.97922 1
 
< 0.1%
88.975494 1
 
< 0.1%
88.96886 1
 
< 0.1%
88.96394 1
 
< 0.1%

Blade2PitchAngle
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39021
Distinct (%)93.1%
Missing76333
Missing (%)64.6%
Infinite0
Infinite (%)0.0%
Mean10.036535
Minimum-26.443415
Maximum90.01783
Zeros20
Zeros (%)< 0.1%
Negative15816
Negative (%)13.4%
Memory size1.8 MiB
2023-05-29T13:00:28.647682image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-26.443415
5-th percentile-0.58513237
Q1-0.43326366
median0.88897652
Q38.4801938
95-th percentile60.000126
Maximum90.01783
Range116.46124
Interquartile range (IQR)8.9134575

Descriptive statistics

Standard deviation20.270465
Coefficient of variation (CV)2.0196676
Kurtosis5.3577154
Mean10.036535
Median Absolute Deviation (MAD)1.4162889
Skewness2.4432174
Sum420440.49
Variance410.89176
MonotonicityNot monotonic
2023-05-29T13:00:28.705180image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.54826 198
 
0.2%
88.548225 189
 
0.2%
90.01783 93
 
0.1%
88.55084 72
 
0.1%
60.000126 41
 
< 0.1%
60.000076 40
 
< 0.1%
84.99981 40
 
< 0.1%
85.00009 40
 
< 0.1%
85 35
 
< 0.1%
-0.52512985 34
 
< 0.1%
Other values (39011) 41109
34.8%
(Missing) 76333
64.6%
ValueCountFrequency (%)
-26.44341475 1
< 0.1%
-2.4062126 1
< 0.1%
-0.9349257 1
< 0.1%
-0.8993303661 1
< 0.1%
-0.877817975 1
< 0.1%
-0.8775155733 1
< 0.1%
-0.8726386996 1
< 0.1%
-0.866457046 1
< 0.1%
-0.86533529 1
< 0.1%
-0.8643328311 1
< 0.1%
ValueCountFrequency (%)
90.01783 93
0.1%
90.01778 1
 
< 0.1%
89.014522 1
 
< 0.1%
88.915344 1
 
< 0.1%
88.84818 1
 
< 0.1%
88.821075 1
 
< 0.1%
88.79701 1
 
< 0.1%
88.786415 1
 
< 0.1%
88.77181 1
 
< 0.1%
88.76847 1
 
< 0.1%

Blade3PitchAngle
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39021
Distinct (%)93.1%
Missing76333
Missing (%)64.6%
Infinite0
Infinite (%)0.0%
Mean10.036535
Minimum-26.443415
Maximum90.01783
Zeros20
Zeros (%)< 0.1%
Negative15816
Negative (%)13.4%
Memory size1.8 MiB
2023-05-29T13:00:28.761608image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-26.443415
5-th percentile-0.58513237
Q1-0.43326366
median0.88897652
Q38.4801938
95-th percentile60.000126
Maximum90.01783
Range116.46124
Interquartile range (IQR)8.9134575

Descriptive statistics

Standard deviation20.270465
Coefficient of variation (CV)2.0196676
Kurtosis5.3577154
Mean10.036535
Median Absolute Deviation (MAD)1.4162889
Skewness2.4432174
Sum420440.49
Variance410.89176
MonotonicityNot monotonic
2023-05-29T13:00:28.819189image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.54826 198
 
0.2%
88.548225 189
 
0.2%
90.01783 93
 
0.1%
88.55084 72
 
0.1%
60.000126 41
 
< 0.1%
60.000076 40
 
< 0.1%
84.99981 40
 
< 0.1%
85.00009 40
 
< 0.1%
85 35
 
< 0.1%
-0.52512985 34
 
< 0.1%
Other values (39011) 41109
34.8%
(Missing) 76333
64.6%
ValueCountFrequency (%)
-26.44341475 1
< 0.1%
-2.4062126 1
< 0.1%
-0.9349257 1
< 0.1%
-0.8993303661 1
< 0.1%
-0.877817975 1
< 0.1%
-0.8775155733 1
< 0.1%
-0.8726386996 1
< 0.1%
-0.866457046 1
< 0.1%
-0.86533529 1
< 0.1%
-0.8643328311 1
< 0.1%
ValueCountFrequency (%)
90.01783 93
0.1%
90.01778 1
 
< 0.1%
89.014522 1
 
< 0.1%
88.915344 1
 
< 0.1%
88.84818 1
 
< 0.1%
88.821075 1
 
< 0.1%
88.79701 1
 
< 0.1%
88.786415 1
 
< 0.1%
88.77181 1
 
< 0.1%
88.76847 1
 
< 0.1%

ControlBoxTemperature
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing56064
Missing (%)47.4%
Memory size1.8 MiB
0.0
62160 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters186480
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 62160
52.6%
(Missing) 56064
47.4%

Length

2023-05-29T13:00:28.869277image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-29T13:00:28.916279image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 62160
100.0%

Most occurring characters

ValueCountFrequency (%)
0 124320
66.7%
. 62160
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124320
66.7%
Other Punctuation 62160
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 124320
100.0%
Other Punctuation
ValueCountFrequency (%)
. 62160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 186480
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 124320
66.7%
. 62160
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 186480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 124320
66.7%
. 62160
33.3%

GearboxBearingTemperature
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct62313
Distinct (%)99.6%
Missing55684
Missing (%)47.1%
Infinite0
Infinite (%)0.0%
Mean64.23417
Minimum0
Maximum82.237932
Zeros226
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-05-29T13:00:28.963157image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile48.848801
Q157.872242
median64.834662
Q371.079306
95-th percentile79.855614
Maximum82.237932
Range82.237932
Interquartile range (IQR)13.207064

Descriptive statistics

Standard deviation10.455556
Coefficient of variation (CV)0.1627725
Kurtosis4.681867
Mean64.23417
Median Absolute Deviation (MAD)6.6202012
Skewness-1.1151655
Sum4017205
Variance109.31866
MonotonicityNot monotonic
2023-05-29T13:00:29.021119image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 226
 
0.2%
67.55545 3
 
< 0.1%
59.57813475 1
 
< 0.1%
56.47278375 1
 
< 0.1%
58.18471157 1
 
< 0.1%
58.49054888 1
 
< 0.1%
58.45084287 1
 
< 0.1%
59.46783055 1
 
< 0.1%
59.58528975 1
 
< 0.1%
59.41420838 1
 
< 0.1%
Other values (62303) 62303
52.7%
(Missing) 55684
47.1%
ValueCountFrequency (%)
0 226
0.2%
18.44888433 1
 
< 0.1%
20.80210867 1
 
< 0.1%
21.93258167 1
 
< 0.1%
23.00795 1
 
< 0.1%
23.89555 1
 
< 0.1%
23.918848 1
 
< 0.1%
24.04009 1
 
< 0.1%
25.15475875 1
 
< 0.1%
25.501198 1
 
< 0.1%
ValueCountFrequency (%)
82.237932 1
< 0.1%
82.2199136 1
< 0.1%
82.2035 1
< 0.1%
82.1906372 1
< 0.1%
82.143782 1
< 0.1%
82.1366646 1
< 0.1%
82.1098775 1
< 0.1%
82.1026621 1
< 0.1%
82.0936836 1
< 0.1%
82.0888711 1
< 0.1%

GearboxOilTemperature
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct62412
Distinct (%)> 99.9%
Missing55786
Missing (%)47.2%
Infinite0
Infinite (%)0.0%
Mean57.561217
Minimum0
Maximum70.764581
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-05-29T13:00:29.080033image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile48.449105
Q153.942181
median57.196089
Q361.305312
95-th percentile68.044944
Maximum70.764581
Range70.764581
Interquartile range (IQR)7.3631311

Descriptive statistics

Standard deviation6.3238949
Coefficient of variation (CV)0.10986382
Kurtosis2.8235064
Mean57.561217
Median Absolute Deviation (MAD)3.6082582
Skewness-0.62964682
Sum3594007.3
Variance39.991647
MonotonicityNot monotonic
2023-05-29T13:00:29.136778image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.83944 7
 
< 0.1%
49.841103 3
 
< 0.1%
57.242847 3
 
< 0.1%
0 3
 
< 0.1%
58.44239 2
 
< 0.1%
59.118063 2
 
< 0.1%
55.309996 2
 
< 0.1%
52.35127 2
 
< 0.1%
61.8030382 2
 
< 0.1%
63.5960325 2
 
< 0.1%
Other values (62402) 62410
52.8%
(Missing) 55786
47.2%
ValueCountFrequency (%)
0 3
< 0.1%
26.79651467 1
 
< 0.1%
26.799627 1
 
< 0.1%
26.8113748 1
 
< 0.1%
26.8187311 1
 
< 0.1%
26.82125144 1
 
< 0.1%
26.83231133 1
 
< 0.1%
26.83423089 1
 
< 0.1%
26.841705 1
 
< 0.1%
26.8448739 1
 
< 0.1%
ValueCountFrequency (%)
70.76458113 1
< 0.1%
70.7387834 1
< 0.1%
70.726373 1
< 0.1%
70.7131655 1
< 0.1%
70.7074472 1
< 0.1%
70.7047985 1
< 0.1%
70.6811602 1
< 0.1%
70.6800354 1
< 0.1%
70.66814578 1
< 0.1%
70.6579964 1
< 0.1%

GeneratorRPM
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct61074
Distinct (%)98.0%
Missing55929
Missing (%)47.3%
Infinite0
Infinite (%)0.0%
Mean1102.0263
Minimum0
Maximum1809.9417
Zeros1037
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-05-29T13:00:29.197816image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.242713
Q11029.8122
median1124.8607
Q31515.402
95-th percentile1754.8505
Maximum1809.9417
Range1809.9417
Interquartile range (IQR)485.58983

Descriptive statistics

Standard deviation528.06395
Coefficient of variation (CV)0.47917546
Kurtosis-0.24832001
Mean1102.0263
Median Absolute Deviation (MAD)301.32826
Skewness-0.78986106
Sum68650726
Variance278851.53
MonotonicityNot monotonic
2023-05-29T13:00:29.256529image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1037
 
0.9%
1029.9807 3
 
< 0.1%
1290.1729 3
 
< 0.1%
1030.0546 3
 
< 0.1%
1029.96825 3
 
< 0.1%
1030.044 3
 
< 0.1%
1029.9868 3
 
< 0.1%
1030.0973 3
 
< 0.1%
1030.0489 2
 
< 0.1%
1030.11225 2
 
< 0.1%
Other values (61064) 61233
51.8%
(Missing) 55929
47.3%
ValueCountFrequency (%)
0 1037
0.9%
0.3403373 1
 
< 0.1%
0.347509225 1
 
< 0.1%
0.3732631444 1
 
< 0.1%
0.3749087778 1
 
< 0.1%
0.3769242091 1
 
< 0.1%
0.3835448 1
 
< 0.1%
0.3925768 1
 
< 0.1%
0.3975889111 1
 
< 0.1%
0.4162656923 1
 
< 0.1%
ValueCountFrequency (%)
1809.9417 1
< 0.1%
1793.65715 1
< 0.1%
1785.7526 1
< 0.1%
1781.184 1
< 0.1%
1779.9696 1
< 0.1%
1777.52295 1
< 0.1%
1776.1821 1
< 0.1%
1775.732767 1
< 0.1%
1773.95117 1
< 0.1%
1773.6787 1
< 0.1%

GeneratorWinding1Temperature
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct62406
Distinct (%)> 99.9%
Missing55797
Missing (%)47.2%
Infinite0
Infinite (%)0.0%
Mean72.460403
Minimum0
Maximum126.77303
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-05-29T13:00:29.318280image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile45.756961
Q155.492241
median65.7888
Q385.867449
95-th percentile116.99198
Maximum126.77303
Range126.77303
Interquartile range (IQR)30.375208

Descriptive statistics

Standard deviation22.627489
Coefficient of variation (CV)0.31227385
Kurtosis-0.46294971
Mean72.460403
Median Absolute Deviation (MAD)12.563402
Skewness0.7500328
Sum4523485.6
Variance512.00327
MonotonicityNot monotonic
2023-05-29T13:00:29.379246image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62.229546 7
 
< 0.1%
0 5
 
< 0.1%
50.16808 3
 
< 0.1%
67.04963 3
 
< 0.1%
114.848114 2
 
< 0.1%
66.437164 2
 
< 0.1%
68.416435 2
 
< 0.1%
90.815475 2
 
< 0.1%
53.27334 2
 
< 0.1%
62.70597 2
 
< 0.1%
Other values (62396) 62397
52.8%
(Missing) 55797
47.2%
ValueCountFrequency (%)
0 5
< 0.1%
27.1780991 1
 
< 0.1%
27.17839578 1
 
< 0.1%
27.20894567 1
 
< 0.1%
27.2155078 1
 
< 0.1%
27.24492711 1
 
< 0.1%
27.28234022 1
 
< 0.1%
27.283243 1
 
< 0.1%
27.3229627 1
 
< 0.1%
27.3632903 1
 
< 0.1%
ValueCountFrequency (%)
126.7730308 1
< 0.1%
126.695327 1
< 0.1%
126.6502432 1
< 0.1%
126.6023713 1
< 0.1%
126.5623484 1
< 0.1%
126.556144 1
< 0.1%
126.5228585 1
< 0.1%
126.5223498 1
< 0.1%
126.5003853 1
< 0.1%
126.4818589 1
< 0.1%

GeneratorWinding2Temperature
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct62424
Distinct (%)> 99.9%
Missing55775
Missing (%)47.2%
Infinite0
Infinite (%)0.0%
Mean71.826659
Minimum0
Maximum126.04302
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-05-29T13:00:29.438527image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile45.287118
Q154.763998
median65.004946
Q385.33774
95-th percentile116.40993
Maximum126.04302
Range126.04302
Interquartile range (IQR)30.573742

Descriptive statistics

Standard deviation22.650255
Coefficient of variation (CV)0.31534608
Kurtosis-0.47247866
Mean71.826659
Median Absolute Deviation (MAD)12.534767
Skewness0.75883912
Sum4485503
Variance513.03407
MonotonicityNot monotonic
2023-05-29T13:00:29.498787image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.686092 7
 
< 0.1%
0 5
 
< 0.1%
49.75333 3
 
< 0.1%
66.4111 3
 
< 0.1%
51.022415 2
 
< 0.1%
61.89688 2
 
< 0.1%
65.577225 2
 
< 0.1%
114.21849 2
 
< 0.1%
68.0335346 2
 
< 0.1%
53.3353473 2
 
< 0.1%
Other values (62414) 62419
52.8%
(Missing) 55775
47.2%
ValueCountFrequency (%)
0 5
< 0.1%
26.9411411 1
 
< 0.1%
26.96148544 1
 
< 0.1%
26.99946544 1
 
< 0.1%
27.03277189 1
 
< 0.1%
27.04038022 1
 
< 0.1%
27.0749868 1
 
< 0.1%
27.1180875 1
 
< 0.1%
27.15934869 1
 
< 0.1%
27.20134823 1
 
< 0.1%
ValueCountFrequency (%)
126.0430176 1
< 0.1%
125.9687988 1
< 0.1%
125.9227522 1
< 0.1%
125.8888851 1
< 0.1%
125.8427976 1
< 0.1%
125.8374666 1
< 0.1%
125.7856394 1
< 0.1%
125.7829339 1
< 0.1%
125.7711597 1
< 0.1%
125.7508729 1
< 0.1%

HubTemperature
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38119
Distinct (%)61.1%
Missing55818
Missing (%)47.2%
Infinite0
Infinite (%)0.0%
Mean36.897978
Minimum0
Maximum47.996185
Zeros305
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-05-29T13:00:29.558493image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.998093
Q133.943949
median37.003815
Q340.008425
95-th percentile44.003815
Maximum47.996185
Range47.996185
Interquartile range (IQR)6.0644753

Descriptive statistics

Standard deviation5.1787109
Coefficient of variation (CV)0.14035216
Kurtosis11.33527
Mean36.897978
Median Absolute Deviation (MAD)3.0172445
Skewness-1.84699
Sum2302655.2
Variance26.819047
MonotonicityNot monotonic
2023-05-29T13:00:29.619399image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.996185 1085
 
0.9%
40.003815 963
 
0.8%
41.996185 932
 
0.8%
43.996185 850
 
0.7%
42.003815 750
 
0.6%
37.996185 682
 
0.6%
36.996185 652
 
0.6%
38.003815 639
 
0.5%
34.996185 544
 
0.5%
37.003815 524
 
0.4%
Other values (38109) 54785
46.3%
(Missing) 55818
47.2%
ValueCountFrequency (%)
0 305
0.3%
3.0937654 1
 
< 0.1%
4.0806705 1
 
< 0.1%
5.6548783 1
 
< 0.1%
7.4043364 1
 
< 0.1%
7.75139275 1
 
< 0.1%
8.219612111 1
 
< 0.1%
10.24810462 1
 
< 0.1%
10.50502857 1
 
< 0.1%
12.67044333 1
 
< 0.1%
ValueCountFrequency (%)
47.996185 2
 
< 0.1%
47.996185 22
< 0.1%
47.9961751 1
 
< 0.1%
47.9959882 1
 
< 0.1%
47.9959267 1
 
< 0.1%
47.9951293 1
 
< 0.1%
47.9950792 1
 
< 0.1%
47.994031 1
 
< 0.1%
47.9938994 1
 
< 0.1%
47.9930207 1
 
< 0.1%

MainBoxTemperature
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct49145
Distinct (%)78.6%
Missing55717
Missing (%)47.1%
Infinite0
Infinite (%)0.0%
Mean39.547603
Minimum0
Maximum54.25
Zeros228
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-05-29T13:00:29.677979image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31.538658
Q135.8125
median39.49131
Q343.359375
95-th percentile48.480221
Maximum54.25
Range54.25
Interquartile range (IQR)7.546875

Descriptive statistics

Standard deviation5.7327829
Coefficient of variation (CV)0.14495905
Kurtosis7.1489476
Mean39.547603
Median Absolute Deviation (MAD)3.7725604
Skewness-1.105137
Sum2472002
Variance32.8648
MonotonicityNot monotonic
2023-05-29T13:00:29.738104image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 228
 
0.2%
39.375 46
 
< 0.1%
39.625 43
 
< 0.1%
39.5625 42
 
< 0.1%
39.3125 42
 
< 0.1%
40 41
 
< 0.1%
39.8125 39
 
< 0.1%
39.1875 39
 
< 0.1%
34.875 38
 
< 0.1%
42.25 38
 
< 0.1%
Other values (49135) 61911
52.4%
(Missing) 55717
47.1%
ValueCountFrequency (%)
0 228
0.2%
14.16666667 1
 
< 0.1%
15.16666667 1
 
< 0.1%
15.22916667 1
 
< 0.1%
15.65 1
 
< 0.1%
15.8875 1
 
< 0.1%
15.9375 1
 
< 0.1%
16.28125 1
 
< 0.1%
16.34375 1
 
< 0.1%
16.46875 1
 
< 0.1%
ValueCountFrequency (%)
54.25 1
 
< 0.1%
54.2310007 1
 
< 0.1%
54.2301537 1
 
< 0.1%
54.18125 1
 
< 0.1%
54.175 1
 
< 0.1%
54.125 1
 
< 0.1%
54.1000004 1
 
< 0.1%
54.0657855 1
 
< 0.1%
54.002976 1
 
< 0.1%
54 4
< 0.1%

NacellePosition
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6664
Distinct (%)9.2%
Missing45946
Missing (%)38.9%
Infinite0
Infinite (%)0.0%
Mean196.29054
Minimum0
Maximum357
Zeros242
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-05-29T13:00:29.796671image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile54.316667
Q1145
median182
Q3271
95-th percentile345
Maximum357
Range357
Interquartile range (IQR)126

Descriptive statistics

Standard deviation88.296554
Coefficient of variation (CV)0.44982583
Kurtosis-0.68001254
Mean196.29054
Median Absolute Deviation (MAD)62.428571
Skewness0.075867101
Sum14187488
Variance7796.2815
MonotonicityNot monotonic
2023-05-29T13:00:29.854754image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178 1343
 
1.1%
188 1289
 
1.1%
172 1249
 
1.1%
175 1201
 
1.0%
182 1184
 
1.0%
185 1089
 
0.9%
166 981
 
0.8%
169 938
 
0.8%
163 926
 
0.8%
160 871
 
0.7%
Other values (6654) 61207
51.8%
(Missing) 45946
38.9%
ValueCountFrequency (%)
0 242
0.2%
0.5 1
 
< 0.1%
0.6 1
 
< 0.1%
1 3
 
< 0.1%
1.2 5
 
< 0.1%
1.285714286 1
 
< 0.1%
1.384615385 1
 
< 0.1%
1.5 46
 
< 0.1%
1.666666667 1
 
< 0.1%
1.714285714 1
 
< 0.1%
ValueCountFrequency (%)
357 254
0.2%
356.5 1
 
< 0.1%
356.4 1
 
< 0.1%
356.25 5
 
< 0.1%
356 5
 
< 0.1%
355.875 1
 
< 0.1%
355.8 3
 
< 0.1%
355.7142857 1
 
< 0.1%
355.6666667 1
 
< 0.1%
355.5 67
 
0.1%

ReactivePower
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct94040
Distinct (%)99.3%
Missing23476
Missing (%)19.9%
Infinite0
Infinite (%)0.0%
Mean88.133966
Minimum-203.18259
Maximum403.71362
Zeros642
Zeros (%)0.5%
Negative30592
Negative (%)25.9%
Memory size1.8 MiB
2023-05-29T13:00:29.916810image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-203.18259
5-th percentile-11.760637
Q1-0.43213689
median35.883659
Q3147.35907
95-th percentile348.32146
Maximum403.71362
Range606.89621
Interquartile range (IQR)147.79121

Descriptive statistics

Standard deviation116.59672
Coefficient of variation (CV)1.3229488
Kurtosis-0.063499324
Mean88.133966
Median Absolute Deviation (MAD)45.078994
Skewness1.1218785
Sum8350517
Variance13594.796
MonotonicityNot monotonic
2023-05-29T13:00:29.973710image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 642
 
0.5%
347.36203 44
 
< 0.1%
-0.00027750563 8
 
< 0.1%
344.7782 5
 
< 0.1%
-0.00022257098 3
 
< 0.1%
141.31088 3
 
< 0.1%
-9.131027 3
 
< 0.1%
350.4541 3
 
< 0.1%
129.84952 2
 
< 0.1%
0.00013087457 2
 
< 0.1%
Other values (94030) 94033
79.5%
(Missing) 23476
 
19.9%
ValueCountFrequency (%)
-203.1825914 1
< 0.1%
-174.5518927 1
< 0.1%
-169.4634724 1
< 0.1%
-117.1323872 1
< 0.1%
-100.1500902 1
< 0.1%
-97.24960768 1
< 0.1%
-94.45873506 1
< 0.1%
-91.37651038 1
< 0.1%
-87.38554 1
< 0.1%
-83.13738821 1
< 0.1%
ValueCountFrequency (%)
403.71362 1
< 0.1%
368.53287 1
< 0.1%
363.3544544 1
< 0.1%
363.3376 1
< 0.1%
361.0306444 1
< 0.1%
359.637151 1
< 0.1%
359.364468 1
< 0.1%
358.0305657 1
< 0.1%
357.56088 1
< 0.1%
355.519659 1
< 0.1%

RotorRPM
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct59254
Distinct (%)95.4%
Missing56097
Missing (%)47.4%
Infinite0
Infinite (%)0.0%
Mean9.9074996
Minimum0
Maximum16.273495
Zeros2809
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-05-29T13:00:30.034628image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.16729308
Q19.2310913
median10.098702
Q313.600413
95-th percentile15.734986
Maximum16.273495
Range16.273495
Interquartile range (IQR)4.3693216

Descriptive statistics

Standard deviation4.7184213
Coefficient of variation (CV)0.47624744
Kurtosis-0.20319364
Mean9.9074996
Median Absolute Deviation (MAD)2.6693097
Skewness-0.80278955
Sum615523.23
Variance22.2635
MonotonicityNot monotonic
2023-05-29T13:00:30.191175image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2809
 
2.4%
11.564648 3
 
< 0.1%
15.64166 2
 
< 0.1%
9.24340475 2
 
< 0.1%
15.7423604 2
 
< 0.1%
9.2365331 2
 
< 0.1%
9.235785 2
 
< 0.1%
15.71970289 2
 
< 0.1%
9.2384215 2
 
< 0.1%
9.23627075 2
 
< 0.1%
Other values (59244) 59299
50.2%
(Missing) 56097
47.4%
ValueCountFrequency (%)
0 2809
2.4%
0.0179586 1
 
< 0.1%
0.01817334783 1
 
< 0.1%
0.02904230133 1
 
< 0.1%
0.0326684 1
 
< 0.1%
0.03492094267 1
 
< 0.1%
0.03532348 1
 
< 0.1%
0.044406277 1
 
< 0.1%
0.0453589 1
 
< 0.1%
0.04704583333 1
 
< 0.1%
ValueCountFrequency (%)
16.273495 1
< 0.1%
16.070668 1
< 0.1%
16.001965 1
< 0.1%
15.994752 1
< 0.1%
15.993397 1
< 0.1%
15.979916 1
< 0.1%
15.94556267 1
< 0.1%
15.94000075 1
< 0.1%
15.91702483 1
< 0.1%
15.9161951 1
< 0.1%

TurbineStatus
Real number (ℝ)

MISSING  SKEWED 

Distinct353
Distinct (%)0.6%
Missing55316
Missing (%)46.8%
Infinite0
Infinite (%)0.0%
Mean2280.4292
Minimum0
Maximum65746528
Zeros203
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-05-29T13:00:30.255295image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median2
Q32
95-th percentile89.6
Maximum65746528
Range65746528
Interquartile range (IQR)0

Descriptive statistics

Standard deviation358603.39
Coefficient of variation (CV)157.25259
Kurtosis31598.056
Mean2280.4292
Median Absolute Deviation (MAD)0
Skewness177.65477
Sum1.4345724 × 108
Variance1.2859639 × 1011
MonotonicityNot monotonic
2023-05-29T13:00:30.313375image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 55073
46.6%
1 2159
 
1.8%
3 1100
 
0.9%
16384 782
 
0.7%
512 286
 
0.2%
4 264
 
0.2%
8192 239
 
0.2%
1024 225
 
0.2%
0 203
 
0.2%
27 155
 
0.1%
Other values (343) 2422
 
2.0%
(Missing) 55316
46.8%
ValueCountFrequency (%)
0 203
 
0.2%
1 2159
 
1.8%
2 55073
46.6%
3 1100
 
0.9%
4 264
 
0.2%
5 2
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
65746528 1
 
< 0.1%
61376732 1
 
< 0.1%
16384 782
0.7%
15604 1
 
< 0.1%
15522 1
 
< 0.1%
15474 2
 
< 0.1%
15124 1
 
< 0.1%
13927 2
 
< 0.1%
13312 1
 
< 0.1%
12544 1
 
< 0.1%

WTG
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
G01
118224 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters354672
Distinct characters3
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowG01
2nd rowG01
3rd rowG01
4th rowG01
5th rowG01

Common Values

ValueCountFrequency (%)
G01 118224
100.0%

Length

2023-05-29T13:00:30.367121image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-29T13:00:30.410359image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
g01 118224
100.0%

Most occurring characters

ValueCountFrequency (%)
G 118224
33.3%
0 118224
33.3%
1 118224
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 236448
66.7%
Uppercase Letter 118224
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 118224
50.0%
1 118224
50.0%
Uppercase Letter
ValueCountFrequency (%)
G 118224
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 236448
66.7%
Latin 118224
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 118224
50.0%
1 118224
50.0%
Latin
ValueCountFrequency (%)
G 118224
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 354672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 118224
33.3%
0 118224
33.3%
1 118224
33.3%

WindDirection
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6664
Distinct (%)9.2%
Missing45946
Missing (%)38.9%
Infinite0
Infinite (%)0.0%
Mean196.29054
Minimum0
Maximum357
Zeros242
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-05-29T13:00:30.454938image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile54.316667
Q1145
median182
Q3271
95-th percentile345
Maximum357
Range357
Interquartile range (IQR)126

Descriptive statistics

Standard deviation88.296554
Coefficient of variation (CV)0.44982583
Kurtosis-0.68001254
Mean196.29054
Median Absolute Deviation (MAD)62.428571
Skewness0.075867101
Sum14187488
Variance7796.2815
MonotonicityNot monotonic
2023-05-29T13:00:30.512953image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178 1343
 
1.1%
188 1289
 
1.1%
172 1249
 
1.1%
175 1201
 
1.0%
182 1184
 
1.0%
185 1089
 
0.9%
166 981
 
0.8%
169 938
 
0.8%
163 926
 
0.8%
160 871
 
0.7%
Other values (6654) 61207
51.8%
(Missing) 45946
38.9%
ValueCountFrequency (%)
0 242
0.2%
0.5 1
 
< 0.1%
0.6 1
 
< 0.1%
1 3
 
< 0.1%
1.2 5
 
< 0.1%
1.285714286 1
 
< 0.1%
1.384615385 1
 
< 0.1%
1.5 46
 
< 0.1%
1.666666667 1
 
< 0.1%
1.714285714 1
 
< 0.1%
ValueCountFrequency (%)
357 254
0.2%
356.5 1
 
< 0.1%
356.4 1
 
< 0.1%
356.25 5
 
< 0.1%
356 5
 
< 0.1%
355.875 1
 
< 0.1%
355.8 3
 
< 0.1%
355.7142857 1
 
< 0.1%
355.6666667 1
 
< 0.1%
355.5 67
 
0.1%

WindSpeed
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct94224
Distinct (%)99.6%
Missing23629
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean5.8789597
Minimum0
Maximum22.970893
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-05-29T13:00:30.574704image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.3565564
Q13.8233299
median5.5577648
Q37.5067098
95-th percentile10.634838
Maximum22.970893
Range22.970893
Interquartile range (IQR)3.6833799

Descriptive statistics

Standard deviation2.6190842
Coefficient of variation (CV)0.4455013
Kurtosis0.63413731
Mean5.8789597
Median Absolute Deviation (MAD)1.8277572
Skewness0.76598886
Sum556120.2
Variance6.8596021
MonotonicityNot monotonic
2023-05-29T13:00:30.635222image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.014299 47
 
< 0.1%
2.5037 33
 
< 0.1%
0 13
 
< 0.1%
2.7512999 12
 
< 0.1%
4.8122997 11
 
< 0.1%
5.0382 10
 
< 0.1%
4.4438 10
 
< 0.1%
4.6636996 9
 
< 0.1%
6.3863 8
 
< 0.1%
3.6682997 7
 
< 0.1%
Other values (94214) 94435
79.9%
(Missing) 23629
 
20.0%
ValueCountFrequency (%)
0 13
< 0.1%
0.9 1
 
< 0.1%
0.957314967 1
 
< 0.1%
0.9623049455 1
 
< 0.1%
0.9802399545 1
 
< 0.1%
0.9999894142 1
 
< 0.1%
1.000244378 1
 
< 0.1%
1.00773494 1
 
< 0.1%
1.022844389 1
 
< 0.1%
1.028254936 1
 
< 0.1%
ValueCountFrequency (%)
22.97089311 1
< 0.1%
22.172939 1
< 0.1%
22.0232 1
< 0.1%
21.3920984 1
< 0.1%
20.17535792 1
< 0.1%
20.12793935 1
< 0.1%
19.9989395 1
< 0.1%
19.94138874 1
< 0.1%
19.55962193 1
< 0.1%
19.49505932 1
< 0.1%

Interactions

2023-05-29T13:00:26.022220image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:05.369222image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:06.472173image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:07.858692image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:08.923223image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:10.201231image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:11.349333image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:12.460310image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:13.683981image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:14.866363image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:16.149322image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:17.257432image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:18.403148image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:19.512712image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-05-29T13:00:23.835233image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-05-29T13:00:17.318328image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:18.459540image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:19.569614image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:20.593046image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:21.654002image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:22.793808image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:23.893058image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:24.931538image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:26.128060image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:05.494543image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:06.578093image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:07.987820image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-05-29T13:00:11.452397image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:12.579503image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:13.795102image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:14.993931image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:16.271792image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:17.398359image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:18.509275image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:19.619785image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:20.644724image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-05-29T13:00:22.848318image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-05-29T13:00:05.551366image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-05-29T13:00:09.156400image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-05-29T13:00:11.502746image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-05-29T13:00:12.293530image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:13.578401image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:14.728269image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:16.035071image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:17.145752image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:18.292104image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:19.405827image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:20.425948image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:21.480507image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:22.623970image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:23.724366image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:24.763928image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:25.910611image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:26.974892image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:06.418531image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:07.674612image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:08.871619image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:10.149503image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:11.162933image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:12.361976image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:13.634469image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:14.810971image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:16.094773image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:17.205448image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:18.350696image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:19.463212image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:20.482819image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:21.539202image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:22.682999image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:23.783833image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:24.821528image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-29T13:00:25.968426image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2023-05-29T13:00:30.703038image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ActivePowerAmbientTemperatueBearingShaftTemperatureBlade1PitchAngleBlade2PitchAngleBlade3PitchAngleGearboxBearingTemperatureGearboxOilTemperatureGeneratorRPMGeneratorWinding1TemperatureGeneratorWinding2TemperatureHubTemperatureMainBoxTemperatureNacellePositionReactivePowerRotorRPMTurbineStatusWindDirectionWindSpeed
ActivePower1.000-0.0860.705-0.344-0.343-0.3430.9120.8740.9840.9140.9140.3270.055-0.0020.7220.984-0.207-0.0020.977
AmbientTemperatue-0.0861.0000.2690.2150.2140.2140.0300.171-0.0490.1030.1050.6520.864-0.049-0.063-0.0490.056-0.049-0.102
BearingShaftTemperature0.7050.2691.000-0.234-0.232-0.2320.8420.7820.7150.8350.8360.7780.4650.1640.6270.715-0.1270.1640.693
Blade1PitchAngle-0.3440.215-0.2341.0000.9740.974-0.328-0.244-0.338-0.269-0.2680.0750.2800.087-0.354-0.3350.2370.087-0.327
Blade2PitchAngle-0.3430.214-0.2320.9741.0001.000-0.325-0.240-0.335-0.266-0.2650.0720.2730.093-0.351-0.3330.2310.093-0.326
Blade3PitchAngle-0.3430.214-0.2320.9741.0001.000-0.325-0.240-0.335-0.266-0.2650.0720.2730.093-0.351-0.3330.2310.093-0.326
GearboxBearingTemperature0.9120.0300.842-0.328-0.325-0.3251.0000.9180.9130.9130.9130.4910.2190.1550.8190.913-0.1850.1550.898
GearboxOilTemperature0.8740.1710.782-0.244-0.240-0.2400.9181.0000.8740.9370.9370.5020.2590.1990.7900.874-0.2070.1990.858
GeneratorRPM0.984-0.0490.715-0.338-0.335-0.3350.9130.8741.0000.9110.9110.3470.0590.1570.8660.997-0.2310.1570.964
GeneratorWinding1Temperature0.9140.1030.835-0.269-0.266-0.2660.9130.9370.9111.0001.0000.5020.2200.1740.8100.912-0.1920.1740.908
GeneratorWinding2Temperature0.9140.1050.836-0.268-0.265-0.2650.9130.9370.9111.0001.0000.5040.2230.1740.8100.912-0.1920.1740.907
HubTemperature0.3270.6520.7780.0750.0720.0720.4910.5020.3470.5020.5041.0000.7610.0990.2900.347-0.0360.0990.309
MainBoxTemperature0.0550.8640.4650.2800.2730.2730.2190.2590.0590.2200.2230.7611.000-0.0380.0320.0620.143-0.0380.049
NacellePosition-0.002-0.0490.1640.0870.0930.0930.1550.1990.1570.1740.1740.099-0.0381.0000.2390.158-0.0231.0000.006
ReactivePower0.722-0.0630.627-0.354-0.351-0.3510.8190.7900.8660.8100.8100.2900.0320.2391.0000.865-0.2050.2390.707
RotorRPM0.984-0.0490.715-0.335-0.333-0.3330.9130.8740.9970.9120.9120.3470.0620.1580.8651.000-0.2240.1580.966
TurbineStatus-0.2070.056-0.1270.2370.2310.231-0.185-0.207-0.231-0.192-0.192-0.0360.143-0.023-0.205-0.2241.000-0.023-0.168
WindDirection-0.002-0.0490.1640.0870.0930.0930.1550.1990.1570.1740.1740.099-0.0381.0000.2390.158-0.0231.0000.006
WindSpeed0.977-0.1020.693-0.327-0.326-0.3260.8980.8580.9640.9080.9070.3090.0490.0060.7070.966-0.1680.0061.000

Missing values

2023-05-29T13:00:27.066947image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-29T13:00:27.288667image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-29T13:00:27.770146image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ActivePowerAmbientTemperatueBearingShaftTemperatureBlade1PitchAngleBlade2PitchAngleBlade3PitchAngleControlBoxTemperatureGearboxBearingTemperatureGearboxOilTemperatureGeneratorRPMGeneratorWinding1TemperatureGeneratorWinding2TemperatureHubTemperatureMainBoxTemperatureNacellePositionReactivePowerRotorRPMTurbineStatusWTGWindDirectionWindSpeed
Date
2017-12-31 00:00:00+00:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNG01NaNNaN
2017-12-31 00:10:00+00:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNG01NaNNaN
2017-12-31 00:20:00+00:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNG01NaNNaN
2017-12-31 00:30:00+00:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNG01NaNNaN
2017-12-31 00:40:00+00:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNG01NaNNaN
2017-12-31 00:50:00+00:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNG01NaNNaN
2017-12-31 01:00:00+00:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNG01NaNNaN
2017-12-31 01:10:00+00:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNG01NaNNaN
2017-12-31 01:20:00+00:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNG01NaNNaN
2017-12-31 01:30:00+00:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNG01NaNNaN
ActivePowerAmbientTemperatueBearingShaftTemperatureBlade1PitchAngleBlade2PitchAngleBlade3PitchAngleControlBoxTemperatureGearboxBearingTemperatureGearboxOilTemperatureGeneratorRPMGeneratorWinding1TemperatureGeneratorWinding2TemperatureHubTemperatureMainBoxTemperatureNacellePositionReactivePowerRotorRPMTurbineStatusWTGWindDirectionWindSpeed
Date
2020-03-30 22:20:00+00:00145.02741527.58399846.3638470.6826221.1158571.1158570.062.24333256.6844901030.09826762.12310361.17403039.98246337.375000188.00000028.7097099.2347512.0G01188.0000003.954384
2020-03-30 22:30:00+00:00147.22369727.57723446.2231710.6332781.0675851.0675850.062.15461356.5140111030.34765661.60434660.65113939.73341037.210633188.00000029.9104689.2309502.0G01188.0000004.190217
2020-03-30 22:40:00+00:00117.70693927.50848946.0889751.0234651.4563491.4563490.061.78941456.2853591029.89133361.05847960.12313839.02452837.034593184.66666722.8465059.2336942.0G01184.6666673.949295
2020-03-30 22:50:00+00:0099.67023727.44042645.9410791.2372701.6716731.6716730.060.79723455.8865931029.65424060.29651159.35976239.03578436.846869178.00000020.1293949.2347922.0G01178.0000003.920965
2020-03-30 23:00:00+00:0090.33106527.58119345.8190841.4118081.8462261.8462260.060.27585155.5520221029.80543359.64873258.72939039.01039436.650659178.00000017.7928889.2352282.0G01178.0000003.612339
2020-03-30 23:10:00+00:0070.04446527.52374145.7111291.5156691.9500881.9500880.059.82116555.1937931029.87074459.06036758.14877739.00893136.476562178.00000013.7757859.2340042.0G01178.0000003.533445
2020-03-30 23:20:00+00:0040.83347427.60288245.5985731.7028092.1367322.1367320.059.14203854.7985451030.16047858.45200357.55036739.00675936.328125178.0000008.0889289.2293702.0G01178.0000003.261231
2020-03-30 23:30:00+00:0020.77779027.56092545.4620451.7062142.1396642.1396640.058.43943954.3804561030.13782258.03407157.09933539.00381536.131944178.0000004.3559789.2368022.0G01178.0000003.331839
2020-03-30 23:40:00+00:0062.09103927.81047245.3438271.5753522.0097812.0097810.058.20541354.0790141030.17817857.79538756.84723939.00381536.007805190.00000012.0180779.2373742.0G01190.0000003.284468
2020-03-30 23:50:00+00:0068.66442527.91582845.2316101.4993231.9331241.9331240.058.58171654.0805051029.83478957.69481356.74104039.00381535.914062203.00000014.4396699.2355322.0G01203.0000003.475205

Duplicate rows

Most frequently occurring

ActivePowerAmbientTemperatueBearingShaftTemperatureBlade1PitchAngleBlade2PitchAngleBlade3PitchAngleControlBoxTemperatureGearboxBearingTemperatureGearboxOilTemperatureGeneratorRPMGeneratorWinding1TemperatureGeneratorWinding2TemperatureHubTemperatureMainBoxTemperatureNacellePositionReactivePowerRotorRPMTurbineStatusWTGWindDirectionWindSpeed# duplicates
46NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNG01NaNNaN22734
22NaNNaN0.0NaNNaNNaN0.00.0NaNNaNNaNNaN0.00.0NaNNaNNaN0.0G01NaNNaN133
201730.66440028.844826NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN268.0347.362030NaNNaNG01268.010.01429944
60.00000027.792393NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN243.00.000000NaNNaNG01243.02.50370024
20.0000000.000000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.000000NaNNaNG010.00.00000013
1-0.000302NaNNaN0.00.00.0NaNNaNNaNNaNNaNNaNNaNNaN163.0-0.000278NaN16384.0G01163.04.8123008
26NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN89.0NaNNaNNaNG0189.0NaN8
30.00000022.403760NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN197.00.000000NaNNaNG01197.04.4438006
100.00000032.645200NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN108.00.000000NaNNaNG01108.05.0382006
40.00000027.792393NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN243.00.000000NaNNaNG01243.02.5037005